Is there any good source for when the pole actually starts all the way at the bottom, in the cartpole problem?
It is difficult to prove a negative, but I doubt there will be a paper on that specific problem. It should be relatively easy to adjust the environment or write a new one that does this if you wished though.

A very similar environment that does have a lot more written about it is Acrobot, which does have a OpenAI Gym version. Instead of a cart on a track with forces applied and a free join to the pole, there is a longer pole fixed to a free joint, with an active joint in the middle (that the agent can apply forces to). It can be thought of as a very simple model of an acrobat on a trapeze swing (with poles instead of chains, so the swing is stable balanced upside-down)

The degrees of freedom and difficulty of the Acrobot task are similar to CartPole - I would rate it as a harder problem overall, but if you started CartPole with the pole hanging down in fact the two problems are very similar. Usually the joint motor is made too weak to achieve the goal of balancing in a single clean action, and the agent must learn to build momentum over a few swings, before moving to the balance point. That also makes both your alternate start position CartPole and Acrobot similar to MountainCar.

For a low-effort look at a very similar problem you could try Acrobot. Otherwise you will likely need to do some custom work on CartPole.